Combination of Neural Network and SBFM Algorithm for Monitoring VOCs Distribution by Open Path FTIR Spectrometry

Abstract

In this research, the combination of artificial neural network (ANN) modeling and smooth basis function minimization (SBFM) algorithm were applied to Open Path Fourier transform infrared spectroscopy (OP‐FTIR) for monitoring volatile organic compounds' concentration distribution in the air. ANN was utilized to analyze the measured mixture spectra containing chloroform, methanol, and methylene chloride; Then, SBFM was used to reconstruct each component's concentration distribution. The peak concentration locations and maximum concentration for three components are reconstructed accurately. The methodology presented in this paper has significant importance in detecting leaking source spot and monitoring airborne VOCs transport in chemical industrial workplaces.

Keywords:

• Atmospheric monitoring
• Open path FTIR spectrometry
• Spectra analysis
• Distribution reconstruction
• BP‐ANN
• SBFM algorithm

Acknowledgments

This project was supported by National Natural Science Foundation of China (No. 20175008), China Postdoctoral Science Foundation (2003034386) and Young Scholar Foundation of Nanjing University of Science and Technology (Njust200303).